def test_find_test_statistic(df, tails):
"""
"""
test_statistic_s = 1.96
p_value = find_p_value(test_statistic_s, df, tails=tails)
test_statistic = find_test_statistic(p_value, df, tails=tails)
assert np.abs(test_statistic - test_statistic_s) <= 1e-10
def test_two_sample_z_test_validated(test1, test2, sample1, sample2, pooled):
"""
Check that the test outputs match validated results within an acceptable margin of error
"""
test_statistic, standard_error = test1(sample1=sample1,
sample2=sample2,
null_h=0.0,
pooled=pooled)
p_value = find_p_value(test_statistic=test_statistic,
df=np.inf,
tails=True)
test_statistic_s, p_value_s = test2(x1=sample1,
x2=sample2,
value=0.0,
usevar="pooled",
alternative="two-sided")
if pooled is True:
assert np.abs(test_statistic - test_statistic_s) <= 1 * 10**(-10)
assert np.abs(p_value - p_value_s) <= 1 * 10**(-10)
else:
assert np.round(np.abs(p_value - p_value_s), 2) <= 1 * 10**(-2)
assert p_value < p_value_s # unpooled p-values consistently skew lower
def test_one_sample_z_test_validated(test1, test2, sample):
"""
Check that the test outputs match validated results within an acceptable margin of error
"""
test_statistic, standard_error = test1(sample=sample, null_h=0.0)
p_value = find_p_value(test_statistic=test_statistic,
df=np.inf,
tails=True)
test_statistic_s, p_value_s = test2(x1=sample, value=0.0)
assert np.abs(test_statistic - test_statistic_s) <= 1 * 10**(-10)
assert np.abs(p_value - p_value_s) <= 1 * 10**(-10)
def test_two_sample_t_test_validated(test1, test2, sample1, sample2, pooled):
"""
Check that the test outputs match validated results within an acceptable margin of error
"""
test_statistic, standard_error, degrees_freedom = test1(sample1=sample1,
sample2=sample2,
null_h=0.0,
pooled=pooled)
p_value = find_p_value(test_statistic=test_statistic,
df=degrees_freedom,
tails=True)
test_statistic_s, p_value_s = test2(a=sample1, b=sample2, equal_var=pooled)
assert np.abs(test_statistic - test_statistic_s) <= 1 * 10**(-10)
assert np.abs(p_value - p_value_s) <= 1 * 10**(-10)
def test_one_sample_z_prop_test_validated(test1, test2, sample):
"""
Check that the test outputs match validated results within an acceptable margin of error
"""
test_statistic, standard_error = test1(sample=sample, null_h=0.0)
p_value = find_p_value(test_statistic=test_statistic,
df=np.inf,
tails=True)
test_statistic_s, p_value_s = test2(count=sample.sum(),
nobs=sample.shape[0],
value=0.0,
alternative="two-sided",
prop_var=False)
assert np.abs(test_statistic - test_statistic_s) <= 1 * 10**(-2)
assert np.abs(p_value - p_value_s) <= 1 * 10**(-2)
def test_md5shuffle(sample, random_flg):
"""
"""
z_statistic = runs_test(sample)
p_value = find_p_value(test_statistic=z_statistic, df=np.inf, tails=True)
assert (p_value > 0.05) == random_flg
